Identifying CLL patients at high risk of atrial fibrillation on treatment using machine learning.

An increased risk of developing atrial fibrillation (AF) has been observed in patients with chronic lymphocytic leukemia (CLL) who were treated with ibrutinib and other BTK inhibitors. Previous studies have explored the prevalence of AF in CLL and the risk of developing AF at time of diagnosis. However, the interaction between treatment type with other risk factors on risk of developing atrial fibrillation at the time of treatment initiation has not been investigated. This becomes particularly crucial in CLL, as there is often a substantial time gap between diagnosis and treatment, unlike many other cancers. We propose a treatment-aware approach using predictive modeling to identify the risk factors associated with AF at time of treatment initiation. Moreover, the model provides treatment-dependent risk factors by including the interaction between the treatment types and other risk factors. The results demonstrated that the treatment-aware modeling including interactions outperformed currentrisk scores.

Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical data.

A highly variable clinical course, immune dysfunction, and a complex genetic blueprint pose challenges for treatment decisions and the management of risk of infection in patients with chronic lymphocytic leukemia (CLL). In recent years, the use of machine learning (ML) technologies has made it possible to attempt to untangle such heterogeneous disease entities. In this study, using 3 classes of variables (international prognostic index for CLL [CLL-IPI] variables, baseline [para]clinical data, and data on recurrent gene mutations), we built ML predictive models to identify the individual risk of 4 clinical outcomes: death, treatment, infection, and the combined outcome of treatment or infection. Using the predictive models, we assessed to what extent the different classes of variables are predictive of the 4 different outcomes, within both a short-term 2-year outlook and a long-term 5-year outlook after CLL diagnosis. By adding the baseline (para)clinical data to CLL-IPI variables, predictive performance was improved, whereas no further improvement was observed when including the data on recurrent genetic mutations. We discovered 2 main clusters of variables predictive of treatment and infection. Further emphasizing the high mortality resulting from infection in CLL, we found a close similarity between variables predictive of infection in the short-term outlook and those predictive of death in the long-term outlook. We conclude that at the time of CLL diagnosis, routine (para)clinical data are more predictive of patient outcome than recurrent mutations. Future studies on modeling genetics and clinical outcome should always consider the inclusion of several (para)clinical data to improve performance.

Artificial intelligence models in chronic lymphocytic leukemia - recommendations toward state-of-the-art.

Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation of these advances to the patient's bedside for AI assisted interventions is not yet the norm. With specific emphasis on CLL, here, we review the progress of prognostic models in hematology and highlight sources of stagnation that may be limiting significant improvements in prognostication in the near future. We discuss issues related to performance, trust, modeling simplicity, and prognostic marker robustness and find that the major limiting factor in progressing toward state-of-the-art prognostication within the hematological community, is not the lack of able AI algorithms but rather, the lack of their adoption. Current models in CLL still deal with the 'average' patient while the use of patient-centric approaches remains absent. Using lessons from research areas where machine learning has become an enabling technology, we derive recommendations and propose methods for achieving state-of-the-art predictions in modeling health data, that can be readily adopted by the CLL modeling community.

Tracheal Sound Analysis for Automatic Detection of Respiratory Depression in Adult Patients during Cataract Surgery under Sedation.

BACKGROUND: Tracheal sound analysis is a simple way to study the abnormalities of upper airway like airway obstruction. Hence, it may be an effective method for detection of alveolar hypoventilation and respiratory depression. This study was designed to investigate the importance of tracheal sound analysis to detect respiratory depression during cataract surgery under sedation. Methods: After Institutional Ethical Committee approval and informed patients' consent, we studied thirty adults American Society of Anesthesiologists I and II patients scheduled for cataract surgery under sedation anesthesia. Recording of tracheal sounds started 1 min before administration of sedative drugs using a microphone. Recorded sounds were examined by the anesthesiologist to detect periods of respiratory depression longer than 10 s. Then, tracheal sound signals converted to spectrogram images, and image processing was done to detect respiratory depression. Finally, depression periods detected from tracheal sound analysis were compared to the depression periods detected by the anesthesiologist. RESULTS: We extracted five features from spectrogram images of tracheal sounds for the detection of respiratory depression. Then, decision tree and support vector machine (SVM) with Radial Basis Function (RBF) kernel were used to classify the data using these features, where the designed decision tree outperforms the SVM with a sensitivity of 89% and specificity of 97%. CONCLUSIONS: The results of this study show that morphological processing of spectrogram images of tracheal sound signals from a microphone placed over suprasternal notch may reliably provide an early warning of respiratory depression and the onset of airway obstruction in patients under sedation.